A typical wireless network includes a multitude of interconnected base stations providing wireless traffic to a varying number of fixed or mobile users distributed over a geographically well-defined coverage area. The wireless interface generally has to operate under conditions including demand for multiple access to the network, uncontrollable signal propagation, and a limited bandwidth. The demand for multiple access to the network means that location and time of service requests are not known a priori. Therefore, the network has to provide the required level of service with sufficient capacity over a large geographical area. The above-noted uncontrollable signal propagation condition indicates that a wireless link between a base station and a user relies on signal propagation in an environment that is typically associated with high propagation loss, and reflection, diffraction, or scattering effects at clutter, terrain, and other types of obstacles.
The combination of these conditions often results in competing design goals. For example, demand for high capacity within a limited bandwidth generally requires operating with high spectral efficiency. This leads to reduced orthogonality among communication channels, resulting in mutual interference due to their overlapping propagation paths in the environment. This interference reduces network coverage area or, equivalently, lowers quality of service. Therefore, the requirement for high area coverage or high quality of service always competes against the demand for high network capacity.
In time division multiple access (TDMA) or frequency division multiple access (FDMA) systems, spectral efficiency can be increased by reducing the frequency reuse factor. This also reduces the average physical distance between cells operating at the same frequency and therefore increases their mutual interference. In code division multiple access (CDMA) systems, the various communication channels are distinguished by codes. Due to propagation effects in the environment, orthogonality between codes may be washed out, such that interference between communication channels increases with traffic load.
Besides spectral efficiency, the amount of traffic that can be handled by the network highly depends on how well the spatial distribution of capacity matches that of the offered traffic load. This sets an additional constraint on allocating and sizing cells in the network, which, of course, is highly dependent on the local propagation environment.
Other constraints that can influence network performance include, e.g., time-dependent variations of the traffic pattern, hardware limitations, external interference effects like thermal noise, morphological issues like requirements for building penetration, etc.
A multitude of other system parameters also have to be considered when a network is designed or adjusted. These parameters include, e.g., base station locations, number of sectors per base station, antenna parameters such as height, orientation, tilt, antenna gain, and antenna pattern, transmit power levels per communication channel and base station, frequency plan, handoff thresholds, number of carriers per base station or sector, etc.
There are underlying constraints associated with some of these parameters, such as base station locations or antenna heights, that may be predetermined by the local morphological environment, e.g., availability of real estate, high buildings for antennas, etc. In addition, certain parameters, such as antenna tilt or antenna orientation, can be easily adjusted in the design phase, but are cost- and time-intensive when they have to be changed afterwards. Other parameters, such as frequency plan, power levels and handoff thresholds, can easily be changed or tuned, even when the network is in service.
As a result of the complexity of the wireless environment, competing design goals such as demand for high capacity and high link performance, and the multitude of system parameters, network design and adjustment are difficult tasks.
Current procedures for network design include design tools that model network performance based on the given network parameters using statistical or other mathematical propagation models. An example of such a design tool is the Planet tool from Mobile Systems International, http://www.rmrdesign.com/msi. These and other convention network design tools calculate certain radio frequency (RF) link metrics, e.g., signal strength or signal-to-interference ratio, which are of significance for particular network performance attributes. The accuracy of these predictions mostly depends on the accuracy of the propagation models and the precision of modeling the environment, e.g., terrain, clutter etc.
Although these conventional tools can provide a sufficiently high accuracy in predicting network performance, they generally do not classify the overall network performance and, therefore, provide no information about how far the network is driven from its optimal state. Due to the complexity of the interactions in the network, tuning network performance has to be done by a trial-and-error procedure, and potential improvements have to be identified by comparing RF link-metric plots for different network configurations. With the number of network parameters that have to be adjusted and the different design goals, this procedure is very unsatisfactory and a performance optimum is difficult to even approach.
Other conventional approaches include frequency planning tools. An example of such a tool is the Asset network design tool, from Aircom, www.aircom.co.uk. The Asset tool includes a frequency planning algorithm. For TDMA and FDMA networks, i.e., networks that have a frequency reuse factor larger than one, many efforts have been made to generate algorithms that improve the network performance with respect to its frequency plan. These algorithms usually have an objective that aims for improvement of spectral efficiency. Such an algorithm, for instance, may try to minimize the amount of frequencies used while serving a given traffic density. These algorithms, however, generally do not provide information about the network performance for each frequency plan, unless they have been linked to a network design tool such as the above-noted PLANET tool.
A well-known conventional approach to network optimization is in-field optimization. Since design tools do not perfectly reflect all propagation effects in the environment, networks can be improved by in-field drive tests. However, drive tests have to be regarded merely as an alternative data acquisition procedure, and inferring network improvements is subjected to the same problems as above. Further, drive-test data can be gained only from limited areas, they are cost and time intensive.
Although many of the above-noted conventional techniques can provide assistance in designing and adjusting a network, they generally do not allow optimization of overall network performance for different mutually competing design goals. A need therefore exists for improved network characterization, adjustment and optimization techniques that can, e.g., unambiguously classify the performance of a wireless network for competing design goals and for different system configurations.